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Background: In the field of antibody engineering, an essential task is to design a novel antibody whose paratopes bind to a specific antigen with correct epitopes. Understanding antibody structure and its paratope can facilitate a mechanistic understanding of its function. Therefore, antibody structure prediction from its sequence alone has always been a highly valuable problem for de novo antibody design. AlphaFold2 (AF2), a breakthrough in the field of structural biology, provides a solution to this protein structure prediction problem by learning a deep learning model. However, the computational efficiency and undesirable prediction accuracy on antibody, especially on the complementarity-determining regions limit its applications in de novo antibody design. Method(s): To learn informative representation of antibodies, we trained a deep antibody language model (ALM) on curated sequences from observed antibody space database via a well-designed transformer model. We also developed a novel model named xTrimoABFold++ to predict antibody structure from antibody sequence only based on the pretrained ALM as well as efficient evoformers and structural modules. The model was trained end-to-end on the antibody structures in PDB by minimizing the ensemble loss of domain-specific focal loss on CDR and the frame aligned point loss. Result(s): xTrimoABFold++ outperforms AF2 and OmegaFold, HelixFold-Single with 30+% improvement on RMSD. Also, it is 151 times faster than AF2 and predicts antibody structure in atomic accuracy within 20 seconds. In recently released antibodies, for example, cemiplimab of PD1 (PDB: 7WVM) and cross-neutralizing antibody 6D6 of SARS-CoV-2 (PDB: 7EAN), the RMSD of xTrimoABFold++ are 0.344 and 0.389 respectively. Conclusion(s): To the best of our knowledge, xTrimoABFold++ achieved the state-of-the-art in antibody structure prediction. Its improvement on both accuracy and efficiency makes it a valuable tool for de novo antibody design, and could make further improvement in immuno-theory.
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Pandemic outbreaks can disrupt firms' normal operations, so they demand a resilient response. Firms can combine social responsibility initiatives with resilient responses by reconfiguring their production resources for pandemic relief. It remains unclear, however, whether pandemic-relieving product adaptation (in short, PRPA) improves financial performance. We draw on stakeholder theory to analyze the effect of a PRPA strategy on the stock returns of US-listed manufacturing firms during the COVID-19 pandemic—the most enduring and large-scale pandemic in recent history. The results reveal that the stock market reacts more positively to PRPA under severe pandemic circumstances and for firms with low political connectedness, low media coverage, and/or more unique production technology. The findings offer important implications for operations theory and practice. © 2023 Decision Sciences Institute.
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China has recently declared its role as a leading developing country in actively practicing carbon neutrality. In fact, its carbon-neutral policy has accelerated from a gradual and macroscopic perspective and has been actively pursued given the changes not only in the overall social system but also in its impact on various stakeholders. This study analyzed the patterns of carbon neutrality (CN) and the actors of policy promotion in China from a long-term perspective. It collected policy discourses related to CN posted on Chinese websites from 2000 to 2022 and conducted text mining and network analysis. The results revealed that the pattern of CN promotion in China followed an exploration-demonstration-industrialization-digitalization model, similar to other policies. Moreover, the policy promotion sector developed in the direction of unification-diversification-specialization. Analysis of policy promotion actors found that enterprises are the key driver of continuous CN. In addition, the public emerged as a critical actor in promoting CN during the 12th-13th Five-Year Plans (2011-2020). Moreover, the central government emerged as a key driving actor of CN during the 14th Five-Year Plan. This was a result of the emphasis on efficiency in the timing and mission process of achieving CN. Furthermore, based on the experience of COVID-19, the rapid transition of Chinese society toward CN emphasizes the need for a central government with strong executive power. Based on these results, this study presents constructive suggestions for carbon-neutral development in China.
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Objective: To investigate epidemiological features of skin damage among front-line healthcare workers fighting against COVID-19 pandemic. Method(s): A self-designed questionnaire was released on an online survey website "wenjuan.com", and sent to the front-line medical staff caring for patients with confirmed COVID-19 in 6 infectious disease wards of the General Hospital of Central Theater Command of PLA via WeChat from March 10th to 20th, 2020. Then, the questionnaires were collected, a database was established, and statistical analysis was performed on the incidence, types and epidemiological characteristics of skin damage among the medical staff. Result(s): A total of about 550 medical staff were surveyed, 404 questionnaires were collected, of which 391 were valid, and 303 cases had skin damage. The survey showed that females, hand cleaning frequency > 10 times per day, wearing three-level protective equipment for more than 6 hours per week were risk factors for skin damage, and frequent use of a hand cream could reduce skin problems. Among the respondents, the incidence of skin damage was significantly higher in the females (79.81%, 249/312) than in the males (38.35%, 54/79;chi2 = 4.741, P = 0.029), and higher in the groups with hand cleaning frequency of 10-20 times per day (79.73%, 118/148) and > 20 times per day (85.71%, 84/98) than in the group with hand cleaning frequency of 1-10 times per day (69.66%, 101/145;chi2 = 9.330, P = 0.009). The incidence of skin damage was significantly lower in the group wearing protective equipment for 1-5 hours per week (64.04%, 73/114) than in the groups wearing protective equipment for 6-10 hours per week (81.48%, 66/81), 11-15 hours per week (95.24%, 20/21), 16-20 hours per week (81.82%, 36/44), 21-25 hours per week (86.49%, 32/37), and > 25 hours per week (80.85%, 76/94;chi2 = 19.164, P = 0.002). Among the 391 respondents, the skin damage related to disinfection and protective equipment mainly manifested as dry skin (72.89%), desquamation (56.78%), skin pressure injury (54.48%), skin maceration (45.01%), and sensitive skin (33.50%);acne (27.11%) was the related skin disease with the highest incidence, followed by facial dermatitis (23.27%), eczematous dermatitis (21.48%), folliculitis (18.92%), dermatomycosis (11.00%), urticaria (9.21%), etc. Conclusion(s): There was a high incidence of skin damage related to protective equipment among the front-line healthcare workers fighting against COVID-19, and strengthening skin protection could markedly reduce the incidence of skin damage.Copyright © 2020 by the Chinese Medical Association.
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Wearing masks can effectively inhibit the spread and damage of COVID-19. A device-edge-cloud collaborative recognition architecture is designed in this paper, and our proposed device-edge-cloud collaborative recognition acceleration method can make full use of the geographically widespread computing resources of devices, edge servers, and cloud clusters. First, we establish a hierarchical collaborative occluded face recognition model, including a lightweight occluded face detection module and a feature-enhanced elastic margin face recognition module, to achieve the accurate localization and precise recognition of occluded faces. Second, considering the responsiveness of occluded face detection services, a context-aware acceleration method is devised for collaborative occluded face recognition to minimize the service delay. Experimental results show that compared with state-of-the-art recognition models, the proposed acceleration method leveraging device-edge-cloud collaborations can effectively reduce the recognition delay by 16%while retaining the equivalent recognition accuracy. IEEE
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This paper applies exogenous shocks to investigate the impact of digital financial inclusion (DFI) on farmers' poverty vulnerability in China. We find that farmers in highly developed DFI areas are less vulnerable to the poverty trap. The result is robust to various checks, including propensity score matching and difference-in-differences method and the instrumental variable approach. Moreover, we find that income diversification is the possible economic channel through which DFI affects farmers' poverty vulnerability. Further analyses show that DFI has a "targeting” effect on those who are poor and vulnerable, and a synergistic effect by working with medical insurance and informal finance in terms of reducing farmers' poverty vulnerability. Our research findings provide new theoretical insights and useful guidance in enhancing financial inclusiveness and sustainable development in the post-COVID-19 era. © 2023 John Wiley & Sons Ltd.
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Introduction: De novo donor specific antibodies (DSAs) are associated with increased risk of antibody-mediated rejection (AMR) and worse prognosis in patients after orthotopic heart transplant (OHT). Viral infections have the potential to induce or reactivate the production of DSAs, yet the development of DSAs after infection by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has not been reported. In this observational study, we describe DSA titers after Coronavirus Disease 2019 (COVID-19) infection and relationship with AMR and graft dysfunction in a large OHT cohort at a tertiary academic medical center. Hypothesis: : We predicted that COVID-19 infection would be associated with development of de novo DSAs or increase in pre-existing DSAs. Method(s): We retrospectively analyzed all adult OHT patients followed at Washington University School of Medicine in St. Louis between 4/1/2020-12/31/2021. COVID-19 infection was defined by positive antigen or PCR test in setting of clinical exposure or symptoms. Patients were considered fully vaccinated 2 weeks after 2 doses of the BNT162b (Pfizer-BioNTech) or mRNA-1273 (Moderna) vaccines or after a single dose of the AD26.COV2.S (Johnson & Johnson) vaccine. De novo DSAs were defined as newly detected MHC I or II antibody greater than 2000 mean fluorescence intensity (MFI) by single antigen beads or newly elevated antibody against angiotensin-II type 1 receptor (AT1R). In patients with pre-existing DSAs, a significant increase was defined by an MFI value that increased by 20% or more compared to their baseline value prior to SARS-CoV-2 infection. Result(s): A total of 577 patients were followed during the study period and 117 cases of COVID-19 infection were identified. Baseline characteristics of COVID-19 positive patients are shown in Figure. Overall, 10% of patients infected with SARS-CoV-2 infection developed de novo DSAs or an increase in pre-existing DSAs, with unvaccinated patients having a higher incidence compared to vaccinated patients (15% vs. 2%, p=0.02). MHC class II-specific antibodies were the most common DSAs detected. There was a trend towards higher incidence of AMR in unvaccinated patients, although mortality and long-term graft dysfunction were similar. Conclusion(s): Unvaccinated patients had a higher incidence of developing de novo or an increase in pre-existing DSAs after SARS-CoV-2 infection. Future studies are necessary to investigate the long-term consequences of COVID-19 in the OHT population.Copyright © 2022
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It is well and widely known that sample pooling could provide an effective and efficient way for fast coronavirus testing among massive asymptomatic individuals. The method of multi-level acceleration for asymptomatic COVID-19 screening has been introduced, and for one and two levels, the optimal group sizes have been obtained. However, there are still multiple challenges. First, it is not clear how to find the optimal group sizes for three or more levels. Second, there is lack of closed-form expressions for the optimal group sizes for two or more levels. Third, it is not clear how to determine the optimal number of levels. And last, it is not known what the maximum achievable speedup is. The motivation of this paper is to address all the above challenges. The optimization of a hierarchical pooling strategy includes its number of levels and the group size of each level. In this paper, based on multi-variable optimization and Taylor approximation, we are able to derive closed-form expressions for the optimal number of levels (Formula presented.), the optimal group sizes (Formula presented.), (Formula presented.),…, (Formula presented.), and the maximum possible speedup of a hierarchical pooling strategy of (Formula presented.), where (Formula presented.) is the fraction of infected people. The above speedup is nearly a linear function of the reciprocal of (Formula presented.), in the sense that it is asymptotically greater than any sub-linear function (Formula presented.) of the reciprocal of (Formula presented.) for any small (Formula presented.). Using the results in this paper, we can quickly and easily predict the performance of an optimal hierarchical pooling strategy. For instance, if the fraction of infected people is 0.0001, an 8-level hierarchical pooling strategy can achieve speedup of nearly 400. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
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The classification of COVID-19 and other viral pneumonias will help doctors to diagnose new coronary patients more accurately and quickly. Aiming at the classification problem of CT in patients with COVID-19, this paper proposes a CT image classification method based on an improved ResNet50 network based on the traditional convolutional neural network classification model. This paper uses the multiscale feature fusion strategy, combined with the improved attention mechanism to obtain the correlation coefficient between the internal feature points of the feature map, and finally achieves the effect of enhancing the representation ability of the feature map. Through the analysis and comparison of the technical principle, classification accuracy, and other parameters, it shows that the improved algorithm has better adaptive ability and classification ability. Through experiments, the improved ResNet50 classification model has a certain improvement in accuracy, time complexity, and spatial complexity compared with the traditional classification model, and the accuracy rate can reach 90.1 %. © 2022 Copyright for this paper by its authors.
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Purpose: Because of the COVID-19, the digital transformation of global hospitality and tourism speeds up. This paper aims to provide comprehensive frame of the digital transformation for further hospitality and tourism research. Design/methodology/approach: Through conducting a critical review of the impact of COVID-19, the current situation about the application of digital technology and digital transformation in hospitality and travel, this study used a qualitative approach to present the viewpoints. Findings: This research presents a theoretical research framework for the hospitality and tourism about digital transformation, including possible directions, contexts and methods. It highlights the importance of digital transformation, and further proposing specific research topics. Research limitations/implications: This research brings valuable implications and guidance for future research from the aspects of key research streams, research context and methodological approaches in hospitality and tourism about digital transformation. Originality/value: This paper supplies existing critical reviewed research through paying attention to the digital transformation approach in hospitality and tourism, providing research guidance technically to the industry of hotels and travel. © 2022, Emerald Publishing Limited.
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SARS-Cov-2 has caused enormous damage to society and put human health at a hazardous level. Optimizing air distribution patterns is one of the most useful manners to minimize the infection risk of susceptible individuals. Mixing ventilation is widely used, but the effect of air supply rate on indoor infection probability has not been studied yet. Three air supply rates, including 576, 864 and 1152 m3/h were adopted to study this problem in a simulated room, with dimensions of 5m×5m×2.7m. The Computational Fluid Dynamics (CFD) method was used to consider indoor flow fields under three cases. The infection probability was calculated by the revised Wells-Riley model. The results showed that the overall infection probability decreased as the air supply rate increased. Meanwhile, the infectious air exhaled by the infector would flow along with the supply airflow in a certain direction, resulting in a nonuniform distribution of infection probability in the room. Increasing air supply rate and optimizing workstation layout may be two useful manners to reduce infection probability in mixing ventilation rooms. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/)
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In the post-COVID-19 era, the market for emergency medical supplies has shrunk, and related production capacity is rapidly overcapacity.The supply chain is at risk of interruption. First, we analyzed the decision-making behavior of supply chain members from the perspective of evolutionary game, and used the probability density function to transform the game model into a sharp mutation model;secondly, we simulated the impact of factors such as cooperative income on catastrophe phenomena;finally, we introduced capacity reserves to advise on the sustainability of emergency supply chains. The results show that: There is a set of divergence points in the emergency supply chain system. When the parameter combination is located in the set sum, the system is prone to mutation phenomenon;¡Before the supply chain state changes abruptly, the cooperation income of the supply chain is increased, and the initial cooperation among members is improved. Probability is the main idea to ensure the sustainability of the supply chain;¢Capacity reserves are conducive to stabilizing cooperation among members, but factors such as reserve scale should also be avoided in the collection of divergence points. © 2022 EDP Sciences. All rights reserved.
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This study examines how job satisfaction in six sub-scales and selected stressors and demographic covariates influenced assisted living administrators' (ALF) intentions to quit during the COVID-19 pandemic. Quantitative and qualitative data were collected from 103 ALF administrators as part of a national study of long-term care administrators' intent to quit during the COVID-19 pandemic funded by the Foundation of the National Association of Long-Term Care Administrator Boards in Washington, DC. Descriptive statistics were collected for the sample, and correlations between variables were examined, as well as responses from 3 open-ended questions that were coded for analysis. Although generally satisfied, roughly 41 percent of ALF administrators reported that they were intending to quit. Qualitative data suggested that job satisfaction was influenced by a more nuanced interpretation of job characteristics and work environment intrinsic factors such as adequacy of staffing and resources, changing regulations during the COVID-19 pandemic, and external supports such as family and friends. Given the limited research on the impact of the COVID-19 pandemic on assisted living communities and their administrators, the results of this study can help to inform policies and strategies for providing support for this segment of long-term services and its workforce during widespread disasters.
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Purpose: Because of the COVID-19, the digital transformation of global hospitality and tourism speeds up. This paper aims to provide comprehensive frame of the digital transformation for further hospitality and tourism research. Design/methodology/approach: Through conducting a critical review of the impact of COVID-19, the current situation about the application of digital technology and digital transformation in hospitality and travel, this study used a qualitative approach to present the viewpoints. Findings: This research presents a theoretical research framework for the hospitality and tourism about digital transformation, including possible directions, contexts and methods. It highlights the importance of digital transformation, and further proposing specific research topics. Research limitations/implications: This research brings valuable implications and guidance for future research from the aspects of key research streams, research context and methodological approaches in hospitality and tourism about digital transformation. Originality/value: This paper supplies existing critical reviewed research through paying attention to the digital transformation approach in hospitality and tourism, providing research guidance technically to the industry of hotels and travel. © 2022, Emerald Publishing Limited.
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Since the outbreak of the COVID-19 epidemic, a large number of disposable protective masks have been manufactured and used, and the abandonment of masks has caused enormous pollution. In this paper the chitosan (CS), polyvinyl alcohol (PVA), and water were used as raw materials and the nanofiber membranes were prepared by electrostatic spinning. The CS/PVA fiber membranes were crosslinked by glutaraldehyde hydrochloric acid vapour. The fiber morphology, hydrolysis resistance, antibacterial properties, chemical structure, thermal stability and filtration performance of nanofiber membranes were characterized. Results shows that the antibacterial performance of the crosslinked composite nanofibers exceeds 97%, the thermal stability is improved, and the fiber morphology is not destroyed. The hybridized fiber membrane has high filtration performance, excellent antibacterial and hydrolysis resistance, which broadening the PVA fiber membrane application. It is expected to replace traditional protective materials and relieve environmental pressure.
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With its extraordinary rapidity of transmission, the COVID-19 pandemic demonstrates the vulnerability of a globalized and networked world. The first months of the pandemic were marked by a significant strain on health-care systems. Since the prospect of pandemics has elevated public health concerns, it is critical to revisit this issue. The primary goal of this essay is to employ data mining technologies and methodologies to do investigative analysis on publicly available information. In this article we shared ways and techniques to handle and control this pandemic in the best possible way using data mining techniques and models. Researchers and scientists will be able to use the results ofour poll to come up with new approaches to combat the pandemic. © The Authors, published by EDP Sciences.
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In the past few years, CRISPR-based (Clustered regularly interspaced short palindromic repeats-based) detection techniques have arisen in the field of bioanalysis, molecular biology and clinical diagnosis. However, there are still some limitations, such as high cost, high technical requirements, complexity, cross contamination, lack of quantification strategies, etc. Microfluidic platforms, as emerging technol-ogies, have the potential to take CRISPR-based technology to the new level in the future. In this review, we illustrate the recent progress as well as our work and collaborative work in CRISPR-based microfluidic detection assays based on the categories of microfluidic chips, including paper-based microfluidics, centrifugal microfluidics, digital microfluidics, electrochemical microfluidics and wearable microfluidics, mainly focusing on how microfluidic platforms improve CRISPR-based detection method in the aspect of visualization, detection throughput, anti-contamination, simple operation, sensitivity, specificity and quantification, etc. And we outlook the future trends of CRISPR-based microfluidic detection methods in both innovation of CRISPR-based and design of microfluidic platforms. (c) 2022 Elsevier B.V. All rights reserved.
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Coronavirus disease 2019 (COVID-19), characterized by high infectivity and invisibility, has spread quickly throughout the world and brought great challenges to hospital security work. Regarding the medical service reality of large general hospitals, the Security Department actively responded to the epidemic prevention and control work. They classified the risk areas in the hospital and formulated corresponding security strategies. Current paper summarize the improvement of the existing security management and control system, the management level of major public health emergencies and the emergency management. Then the control measures for scientific deployment of personnel and proper planning of the diagnosis and treatment process of risk patients during the epidemic are discussed. This study aimed to provide helpful reference for ensuring the stability of medical reception order in large general hospitals under COVID-19 epidemic. Copyright © 2022, Editorial Board of Journal of Xi'an Jiaotong University (Medical Sciences). All right reserved.
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Background: Severe acute respiratory syndrome coronavirus 2 is a highly contagious viral infection, without any available targeted therapies. The high mortality rate of COVID-19 is speculated to be related to immune damage. Methods: In this study, clinical bioinformatics analysis was conducted on transcriptome data of coronavirus infection. Results: Bioinformatics analysis revealed that the complex immune injury induced by coronavirus infection provoked dysfunction of numerous immune-related molecules and signaling pathways, including immune cells and toll-like receptor cascades. Production of numerous cytokines through the Th17 signaling pathway led to elevation in plasma levels of cytokines (including IL6, NF-kB, and TNF-a) followed by concurrent inflammatory storm, which mediates the autoimmune response. Several novel medications seemed to display therapeutic effects on immune damage associated with coronavirus infection. Conclusions: This study provided insights for further large-scale studies on the target therapy on reconciliation of immunological damage associated with COVID-19. Copyright © 2021 Shandong University, published by Wolters Kluwer, Inc.